Random Generation of Complex Data Structures for the Simulation of Construction Operations
Bibliographic record
Abstract
Construction production systems are complex in nature and possess a high level of uniqueness. Modelling and simulation of such systems is challenging due to the randomness, complexity, and interdependency associated with many factors such as the type of product created, the steps of the production process, and the medium or the environment hosting the production process. These factors represent or control the working behaviour of a construction system and need to be realistically represented in a model in order to achieve accurate replication of real system behaviours. However, modeling and simulation of these factors require either a rich real life data set, which is seldom available for construction operations, or random generation of complex data structures with highly correlated attributes. This paper presents an investigation of mathematical techniques that can be used to generate random complex data structures while preserving the correlations between the embedded attributes. Generation of weather and pipelines data sets are selected in this study. We propose a non-parametric approach in the weather generation; its performance is measured against a parametric approach. For the generation of pipeline data sets, we propose a generation methodology based on a Markov chain model for a pipeline structure. It represents part of an ongoing research. A detailed description of the methodology and the progress in this part of the study are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".